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brahmandam/DoomFrameDataset

DoomFrameDataset DoomFrameDataset is a ViZDoom frame-action dataset generated from policy rollouts. It is packaged as WebDataset tar shards for streaming training, imitation learning, behavior cloning, and offline reinforcement-learning experiments. The dataset contains RGB game frames paired with the action selected by the rollout policy and per-step metadata such as reward, episode id, step id, terminal flag, and value estimate. Dataset Size Config Files… See the full description on the dataset page: https://huggingface.co/datasets/brahmandam/DoomFrameDataset.

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Dataset Card

DoomFrameDataset

DoomFrameDataset is a ViZDoom frame-action dataset generated from policy rollouts. It is packaged as WebDataset tar shards for streaming training, imitation learning, behavior cloning, and offline reinforcement-learning experiments.

The dataset contains RGB game frames paired with the action selected by the rollout policy and per-step metadata such as reward, episode id, step id, terminal flag, and value estimate.

Dataset Size

Config	Files	Samples	Intended use
`preview`	1 shard	~79k	Hugging Face preview and quick sanity checks
`full`	31 shards	2,398,745	Training and full streaming reads

The packaged dataset is about 68 GB.

Files

text

data/
  train-000000.tar
  train-000001.tar
  ...
  train-000030.tar
action_map.json
README.md

Each tar shard contains paired files with the same numeric key:

text

000000000000.png
000000000000.json
000000000001.png
000000000001.json
...

The PNG is the game frame. The JSON is the metadata for that frame.

Sample Metadata

json

{
  "action_id": 1,
  "action_name": "TURN_RIGHT",
  "action_vector": [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
  "curriculum_level": 1,
  "done": false,
  "episode": 1,
  "frame_path": "frames/episode_001/step_000000.png",
  "global_step": 0,
  "reward": 0.0,
  "source_frame_path": "frames/episode_001/step_000000.png",
  "step": 0,
  "value": 1.7968196868896484,
  "webdataset_key": "000000000000"
}

See action_map.json for the full action id, action name, and action vector mapping.

Load The Preview Config

Use preview when you only want to verify the dataset or inspect examples in the Hugging Face Dataset Viewer.

python

from datasets import load_dataset

ds = load_dataset(
    "brahmandam/DoomFrameDataset",
    "preview",
    split="train",
    streaming=True,
)

sample = next(iter(ds))
print(sample.keys())
print(sample["json"])
image = sample["png"]

Stream The Full Dataset

Use full for training.

python

from datasets import load_dataset

ds = load_dataset(
    "brahmandam/DoomFrameDataset",
    "full",
    split="train",
    streaming=True,
)

for sample in ds:
    image = sample["png"]
    metadata = sample["json"]
    action_id = metadata["action_id"]
    break

You can also read the shards directly with WebDataset:

python

import webdataset as wds

urls = "https://huggingface.co/datasets/brahmandam/DoomFrameDataset/resolve/main/data/train-{000000..000030}.tar"

dataset = (
    wds.WebDataset(urls)
    .decode("pil")
    .to_tuple("png", "json")
)

image, metadata = next(iter(dataset))

Notes

The preview config intentionally points to a single shard so the Hub can inspect a small part of the dataset without processing the full 68 GB. For training, use the full config.

This dataset was generated from automated ViZDoom policy rollouts. It should be treated as gameplay observation/action data, not human demonstrations.